Technical Field
The present invention relates to online learning assessments. More specifically, embodiments of the present invention relate to predicting student proficiencies in certain specific knowledge components using online assessments. The predicted values of proficiencies for multiple knowledge components create a knowledge profile for each student. The personalized knowledge profile of the student drives the personalized content recommendations to bridge the identified knowledge gaps.
Related Art
Online courses typically have hundreds of knowledge components that are hierarchically organized as units or chapters. These knowledge components typically comprise a skill or learning objective that a student must master in order to meet course objectives. Proficiency in a particular knowledge component involves quantitatively measuring how much the student has learned the particular knowledge component. More specifically, proficiency in a particular knowledge component is the probability that a student can successfully solve a problem designed to assess the particular knowledge component.
Proficiency in a knowledge component in an online course is typically measured using computer adaptive online assessment. During the assessment, the student responses are evaluated after each assessment item is answered to measure proficiencies in various knowledge components. The subsequent assessment items and remedial content are recommended to the student based on how much the student has mastered each particular knowledge component. Therefore, accurate prediction of student's proficiency in different knowledge component is critical for a personalized course.
Accurate prediction of a student's proficiency and its application in a personalized course has numerous challenges. For example, sparse student response presents a significant challenge to predicting a student's proficiency. Accurate prediction may be possible if a student has answered sufficiently large number of assessment items that assess a knowledge component. However, in order to optimize the time available during the assessment and begin content recommendation for the student, proficiency prediction is required based on student responses to only a few assessment items.
A second challenge associated with accurate prediction of a student's proficiency involves proficiency prediction during ongoing learning. For example, proficiency prediction models such as Item Response Theory (IRT) are widely available for computer adaptive testing. Models such as IRT, use student responses to predict proficiency at the course level. These models are good for capturing overall student proficiency in a course at a given time. Therefore, such models are popular for standardized testing and summative reporting of student performance in a course, when a grade or a percentile score is needed. However, during the formative part of the course while students are constantly learning and mastering knowledge components, these models are not very useful. In order to recommend appropriate content, it is important to know if a student has learned a knowledge component or not.
Finally, mastery of concepts presents problems with traditional proficiency prediction systems. Traditional proficiency prediction models, such as IRT, tend to categorize students along a normal distribution bell curve for the population. For standardized testing, such as the SAT or the GMAT, or the summative part of the course, this approach works. However, traditional proficiency prediction systems are not very helpful for the formative part of the course when the students are engaged in acquiring knowledge.